class_docstr: The BLiMP linguistic knowledge evaluation suite from this paper:
  https://aclanthology.org/2020.tacl-1.25.pdf
  
  BLiMP evaluates the linguistic knowledge of language models in terms of syntax, morphology,
  and semantics. The dataset covers 12 linguistic phenomena and 67 paradigms. There are 1000
  examples for each paradigm. Every example is a minimal pair that includes a good sentence
  and a bad sentence. The model is considered correct if it assigns a higher probability to
  the good sentence instead of the bad one.
  
  e.g. Good: Craig explored that grocery store. Bad: Craig explored that grocery stores.
code: def get_instances(self) -> List[Instance]:
      # Download the raw data
      data_path: str = os.path.join(self.output_path, "data")
      ensure_file_downloaded(
          source_url="https://github.com/alexwarstadt/blimp/blob/master/BLiMP.zip?raw=true",
          target_path=data_path,
          unpack=True,
          unpack_type="unzip",
      )
  
      # Read all the instances
      instances: List[Instance] = []
      for paradigm in self.phenomenon_to_paradigms[self.phenomenon]:
          jsonl_path: str = os.path.join(
              data_path, "data", f"{paradigm}.jsonl"
          )
          hlog(f"Reading {jsonl_path}")
          with open(jsonl_path) as f:
              for line in f:
                  # Example: {"sentence_good": "Who should Derek hug after shocking Richard?",
                  # "sentence_bad": "Who should Derek hug Richard after shocking?",
                  # "field": "syntax", "linguistics_term": "island_effects", "UID": "adjunct_island",
                  # "simple_LM_method": true, "one_prefix_method": false, "two_prefix_method": false,
                  # "lexically_identical": true, "pairID": "0"}
                  example = json.loads(line)
                  instance = Instance(
                      input=Input(text=""),
                      references=[
                          Reference(
                              Output(text=example["sentence_good"]),
                              tags=[CORRECT_TAG],
                          ),
                          Reference(
                              Output(text=example["sentence_bad"]),
                              tags=[],
                          ),
                      ],
                      split=TEST_SPLIT,
                  )
                  instances.append(instance)
      return instances
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/blimp_scenario.py
imports: import json
  import os
  from typing import List
  from helm.common.general import ensure_file_downloaded
  from helm.common.hierarchical_logger import hlog
  from .scenario import CORRECT_TAG, Reference, Scenario, Instance, Input, TEST_SPLIT, Output
label_desc: Use image node_id 2 for calling the BLiMPScenario obj's underlying member method code with example usage: obj.get_instances() and returns: instances